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07 December 2021 | Story Nonsindiso Qwabe
Christa Faber
Innovative Methods in Assessment Practices award winner for the Qwaqwa Campus, Christa Faber.

By working with students and being part of their development into successful young adults, Mathematics and Applied Mathematics Lecturer on the Qwaqwa Campus, Christa Faber, soon realised that she would like to proceed with her own studies, and she set her sights on just that. Obtaining her honours degree in Mathematical Statistics at age 40 inspired Faber to continue pursuing an education. She will be receiving her Master of Higher Education Studies degree during the December graduations.

Teaching has always been her passion, Faber shared fondly. She commenced her teaching career as a Mathematics teacher in a small town, Molteno, in the Eastern Cape. After four years of teaching, she worked as a Mathematics supply teacher in the United Kingdom for two years. Upon her return, she continued her teaching career in Harrismith, where she was appointed as a Science teacher at Harrismith High School, before receiving an offer to assist the UFS Qwaqwa Campus as a Statistics facilitator in 2003. She never looked back.

As a researcher, Faber has spent the past eight years using technology as an educational tool to determine whether it can be used to improve students’ performance and understanding of basic statistics. “I believe students learn best when they expect to be successful and see the value of the course for their personal development,” she said.

Faber conducted an experiment on how an online assessment tool (OAT) could be incorporated into the Statistics module to enhance student engagement, and consequently, the performance of students in a rural setting. The transition from face-to-face teaching to online learning has been a topic across all institutions of higher learning, with students’ response to learning on blended platforms being of great importance.

The learning experiment, conducted pre-COVID, showed the benefits that online assessment tools could have on the performance and engagement of students at a rural university. Faber said she considers it important to know how students engaged in key online and general learning practices as a way of managing and developing rural university education. For the experiment, a pragmatic parallel mixed methods design was used to divide students into two groups to compare the performances of those with online assessment tool interventions and those without.

The intervention recently won Faber the Innovative Methods in Assessment Practices award for the Qwaqwa Campus at this year’s Centre for Teaching and Learning awards. The purpose of the category was to showcase how assessment strategies, tools, and assessment activities are used to assess students in new, original, or inventive ways. She said she was grateful to receive recognition for a research project inspired by her passion for teaching and learning, combined with the use of online assessment technology, to enhance students’ learning experience in the field of statistics. “My ongoing research supports the promotion of student engagement in statistics education, as well as in the general educational field.”

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Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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